Multi-Person 2D Pose Estimation. As mentioned, existing methods for multi-person 2D pose estimation can mainly be divided into top-down and bottom-up approaches. Typical top-down frameworks deal with human detection and pose estimation in two stages [1]}, [2]}. Despite their strength at handling scale variation, top-down methods also suffer from high computational redundancy when attempting to detect additional persons. Moreover, their performance tends to degrade severely with heavy occlusion since they have no awareness of out-of-patch contexts. Bottom-up approaches [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]} localize all key points in the image first and then group them to an individual. Examples include OpenPose [3]}, a representative work, which was the first to present the part-affinity field approach that links key points likely to lie in the same person. Zhou et al. [11]} were the first to propose a single-stage offset-based method for 2D human pose estimation. However, the above method only focuses on the 2D level and is careless about 3D information from the image.
